1. Field of the Invention
The present invention relates to an image monitoring system and an object area tracking method suitable for application to a monitoring system using an ITV camera, for example, and particularly to an image monitoring system and the like that in a tracking process performed by associating a current object area and a past object area with each other using unique identifying information, continue retaining identifying information given to a predetermined object area that has disappeared and give the retained identifying information to the predetermined object area when the predetermined object area reappears. The identifying information can be maintained even when the object area temporarily disappears because the object area is overlapped or hidden, and thus object tracking performance is improved.
2. Description of the Related Art
Technology of detecting objects such as people, cars and the like in an image taken by an ITV (Industrial TV) camera or the like is very important in constructing an image monitoring system. For example, when the technology is applied to a monitoring system using an ITV camera, the construction of the system is considered which takes an image of a place desired to be monitored, such as an off-limits area or the like, by a camera, detects whether an object is present in the image, and generates an alarm when an object enters. In addition, at a place where a large number of people enter and leave a department store or a railway station, a flow of the people is surveyed by tracking the moving people. Thus, the technology can be applied also to regulation of a flow of people, market research and the like.
To detect a moving object in an image may require two processes as shown in FIG. 7. First, an object area detecting process is performed on the basis of an input image signal to detect information on an object area (current object area) from an image based on the input image signal. Next, in an object area tracking process, the current object area and a past object area are associated with each other, and object area information (size, position, traveling velocity, identifying number and the like) is output. The identifying number constitutes unique identifying information. By performing these processes, it is possible to detect a moving object present in an image.
As the object area detecting process, there is a method of comparing an input image (current image) and a past image with each other and thereby detecting a change area as an object area, as described in Japanese Patent Laid-Open No. Hei 6-1100552 referred to as Patent Document 1, for example. This kind of method is generally referred to as inter-frame difference processing.
In addition, as the object area detecting process, there is for example a method described in C. Stuffer, W. E. L Grimson “Adaptive background mixture models for real-time tracking”. This method generates a background image that does not include an object area, compares the background image with an input image, and thereby detects the object area. This kind of method is generally referred to as background difference processing.
Further, as the object area detecting process, a processing method as a combination of the inter-frame difference processing and the background difference processing as described above has been proposed. The method is described in Japanese Patent Laid-Open No. 2004-282368 referred to as Patent Document 2, for example. By using such processes, an object area in an input image can be detected. This process first determines a size, a position and the like of information on the object area.
The object area tracking process tracks the object area by associating a current object area and a past object area detected from images based on an input image signal with each other, and gives a unique identifying number to the object area being tracked. There is a method as follows for the association. For example, an object area tracking process disclosed in Japanese Patent Laid-Open No. 2001-325672 referred to as Patent Document 3 evaluates correlation between an object area detected in a current image and an object area detected in a past image using size, position and the like, and associates object areas having a highest evaluation value with each other.